ln-723-seed-data-generator

Paths: File paths ( shared/ , references/ , ../ln- ) are relative to skills repo root. If not found at CWD, locate this SKILL.md directory and go up one level for repo root. ln-723-seed-data-generator Type: L3 Worker Category: 7XX Project Bootstrap Parent: ln-720-structure-migrator Universal seed data generator with two modes: MIGRATE (parse existing ORM schemas) or GENERATE (create from entity definitions). Outputs to any target format (C#, TypeScript, Python, JSON, SQL). Purpose & Scope Aspect Description Input ORM schema files (MIGRATE) or entity list (GENERATE) Output Seed data files in target format Modes MIGRATE: parse existing ORM → seed data. GENERATE: entity definitions → seed data Scope boundaries: Parses ORM schema definitions or accepts entity lists Generates seed data in requested target format Creates realistic sample data using faker libraries Does not generate database migrations, EF Core configs, or ORM models Mode Selection Mode When Input Source MIGRATE TRANSFORM pipeline — existing ORM schemas found ORM schema files Drizzle, Prisma, TypeORM, EF Core, SQLAlchemy, Django ORM GENERATE CREATE pipeline — no existing schemas Entity list from ln-700 Phase 0 User-provided or starter template (User, Role) If GENERATE mode receives no entity list, generate starter template with User (id, name, email, role, createdAt) and Role (id, name, description). Target Formats Format Output File Faker Library Use Case C# MockData MockData.cs Bogus .NET projects TypeScript fixtures seed.ts Faker.js Node/React projects Python factories factories.py Faker (Python) Django/Flask projects JSON seed.json — API testing, import scripts SQL seed.sql — Direct DB seeding Workflow Phase Name Actions Output 1 Parse/Define 1A: Parse ORM schema (MIGRATE) or 1B: Accept entity list (GENERATE) Entity model 2 Map Types Apply universal type mapping to target format Target type definitions 3 Generate Seed Data Create seed files with faker-based realistic data Seed data files 4 Verify Validate relationships, check syntax Valid seed files Phase 1: Parse/Define 1A: MIGRATE Mode — Parse ORM Schema Step Action Reference 1A.1 Locate schema file(s) — 1A.2 Auto-detect ORM type orm_patterns.md — ORM Auto-Detection table 1A.3 Extract table/model definitions orm_patterns.md — per-ORM parsing section 1A.4 Extract column definitions with types orm_patterns.md 1A.5 Identify constraints (PK, FK, nullable, unique) orm_patterns.md 1A.6 Extract enum definitions orm_patterns.md 1B: GENERATE Mode — Accept Entity Definitions Step Action Reference 1B.1 Receive entity list from orchestrator (or use starter template) — 1B.2 Parse entity definitions (name, fields, types) — 1B.3 Infer relationships from field names ( userId → FK to User) relationship_mapping.md 1B.4 Apply default constraints (id = PK, Id = FK) — Output: Entity model with columns, types, and constraints. Phase 2: Map Types Convert entity types to target format types. Step Action Reference 2.1 Select target format (from orchestrator params) — 2.2 Map column types to target format type_mapping.md — Universal Type Mapping table 2.3 Determine nullable status per target type_mapping.md 2.4 Map foreign keys and relationships relationship_mapping.md 2.5 Transform names to target convention See Name Conventions table below Name Conventions by Target: Target Class/Model Property/Field File C# PascalCase singular PascalCase PascalCase.cs TypeScript PascalCase singular camelCase camelCase.ts Python PascalCase singular snake_case snake_case.py JSON camelCase camelCase kebab-case.json SQL snake_case plural snake_case snake_case.sql Phase 3: Generate Seed Data Create seed files with realistic data using faker libraries. Step Action Reference 3.1 Determine generation order (parents → children) relationship_mapping.md 3.2 Generate IDs (GUIDs/UUIDs) for all entities data_generation.md 3.3 Generate field values using faker data_generation.md , type_mapping.md — Faker Integration 3.4 Ensure FK relationships valid (child references existing parent ID) relationship_mapping.md 3.5 Write seed file in target format — Faker integration rule: All generated seed files MUST use faker libraries for realistic data with deterministic seeding (fixed seed value for reproducibility). Target Faker Setup C# var faker = new Bogus.Faker(); Randomizer.Seed = new Random(42); TypeScript import { faker } from '@faker-js/faker'; faker.seed(42); Python from faker import Faker; fake = Faker(); Faker.seed(42) Generation order by dependency: Order Entity Type Generate After 1 Root entities (no FK) First 2 First-level children Parents exist 3 Second-level children Grandparents exist N Deepest children All ancestors exist Phase 4: Verify Check Method Expected Syntax valid Language-specific check No syntax errors FKs valid Cross-reference All FKs point to existing IDs Types correct Type analysis Proper types for target format Names follow convention Pattern check Per-target naming convention Faker deterministic Re-run with same seed Identical output Supported ORM Detection ORM Detection Pattern Ecosystem Drizzle pgTable() , mysqlTable() , sqliteTable() Node.js Prisma model X { syntax in .prisma files Node.js TypeORM @Entity() , @Column() decorators Node.js EF Core DbContext , DbSet<> , [Table] attributes .NET SQLAlchemy Base = declarative_base() , Column() Python Django ORM models.Model , models.CharField() Python Entity Transformation Rules Source Target Transformation Table name (plural, snake) Class name (singular, Pascal) user_profiles → UserProfile Column name (snake) Property name (target convention) created_at → CreatedAt / createdAt / created_at Enum name Enum type (Pascal) status_enum → StatusEnum FK column Navigation property user_id → UserId / userId Sample Data Guidelines Field Type Sample Count Distribution Root entities 3-5 items Varied status/priority Child entities 5-10 items Distributed across parents Leaf entities 10-20 items Realistic variety Critical Rules Single Responsibility: Generate only seed data, no ORM models or migrations Idempotent: Can re-run with same seed to produce identical output Valid Relationships: All FKs must reference existing parent IDs Faker Required: Use faker libraries for realistic data, never random strings Deterministic Seeding: Fixed seed value (42) for reproducibility across re-runs Generation Order: Parents before children, always Mode Awareness: MIGRATE parses files; GENERATE accepts definitions — never mix Definition of Done Mode determined (MIGRATE or GENERATE) Entity model extracted/defined with all fields and constraints Target format selected and type mappings applied Seed data files generated with faker-based realistic values Deterministic seeding verified (re-run produces identical output) Foreign keys reference valid parent IDs Names follow target format conventions Sample data includes 5-10 items per entity Risk Mitigation Risk Detection Mitigation Unknown ORM type Auto-detection fails Log warning, ask orchestrator for ORM hint Invalid type mapping Unknown column type Use string as fallback, log warning FK mismatch FK references non-existent ID Generate parents first, validate after No entity list in GENERATE Empty input Use starter template (User, Role) Name collision Duplicate class/table names Prefix with feature name Circular references Self-referencing with cycles Limit depth, validate graph Reference Files File Purpose references/orm_patterns.md ORM auto-detection and schema parsing patterns (Drizzle, Prisma, TypeORM, EF Core, SQLAlchemy, Django) references/type_mapping.md Universal type mapping (ORM-agnostic → C#, TypeScript, Python) + Faker integration references/data_generation.md Realistic sample data patterns and generation rules references/relationship_mapping.md FK handling, generation order, relationship inference Version: 3.0.0 Last Updated: 2026-02-07